A predominant navigation system for unmanned aerial vehicles (UAVs) today is based on inertial navigation system (INS) and global navigation satellite system (GNSS) integration. GNSS provides absolute time, position, and velocity (TPV) data for the UAV at a relatively low frequency of only a few hertz (Hz), while INS provides position and attitude data at much higher frequencies than GNSS, typically at a few hundred Hz. The integration of INS and GNSS data can provide solutions with short-term and long-term accuracy. However, problems arise when GNSS outage occurs, which is not rare and can happen due to intentional corruption of GNSS signal reception (i.e., jamming or spoofing) or unintentionally (i.e., loss of line of sight to the satellites or interference in satellite signal reception). When GNSS outage occurs, navigation is based solely on INS with possible aiding from aids such as barometric altimeters. The accuracy of the data provided by INS is directly determined by the quality of an inertial measurement unit (IMU) used in the system. The long-term accuracy of three dimensional (3D) inertial coasting is low, especially when based on small and low-cost IMUs presently available for small UAVs. Thus, after only a few minutes of GNSS outage, the position uncertainty of the UAV makes the UAV navigation solution far from being of practical use. In other words, unless drift is controlled by other means, the UAV is completely lost in space or may even become dynamically unstable. This may cause serious problems, especially in non-line-of-sight flights, and can lead to loss of the UAV with possible damage to people, animals, or other objects on the ground.
Several approaches have been proposed to address the aforementioned problem of rapid drift during GNSS outage conditions, with each having their own drawbacks. One approach has tried to improve INS error modeling using advanced sensor error modeling techniques, while another approach has chosen to employ additional navigation sensors to aid the navigation system. The former approach fails to provide sufficient improvements for UAVs. The latter approach adds cost and complexity to the UAV and, more importantly, UAV performance according to the second approach depends on environmental conditions that are not always met. This challenges the navigation autonomy of the UAV.
A widely used (yet partial) solution to the aforementioned second approach employs vision based methods that provide relative or absolute measurements to navigation. Apart from adding extra weight and hardware and software complications, correct functioning of the vision based methods requires prerequisites regarding light, visibility, and terrain texture. For example, the vision based methods might not work at night, in foggy conditions, or over ground with uniform texture (e.g., vegetation, water, snow, etc.).
Other proposed solutions integrate vehicle dynamic models (VDMs) to improve navigation accuracy, especially in GNSS outage conditions. Most of these techniques employ kinematic modeling (i.e., INS) as the main process model within a navigation filter, while using the VDM output either in a prediction phase or in an update phase within the navigation filter. Hence, approaches utilizing VDMs rely entirely on the IMU and therefore are not robust if IMU failure occurs. Other solutions have considered using both INS and VDM at the same level within the navigation filter but, like the aforementioned VDM methodology, these solutions are also based on navigation filtered INS output. Therefore, IMU failure disables navigation in these solutions as well.